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Multiple data sources enable us to grasp different pieces of information which each partially characterize what is happening on the earth's surface differently. Combining all these datasets, however, is a challenging task from a data analysis point of view. In the current big data scenario, we have massive amounts of data generated by complex underlying physical processes which are difficult to monitor. The extraction of meaningful information from these large-scale heterogeneous datasets is an open problem. The second big challenge is then to find out levels at which these datasets can be combined - raw or pixel level, feature level, or at the decision level. Moreover, most of the current data fusion techniques are based on data-driven Artificial Intelligence (AI) methods which fail to provide transparency and explainability of the AI algorithm. In other words, it does not provide us with a clear physical meaning of what the results mean and why the model makes that prediction. \nThe Ph.D. project aims at understanding some of the challenges and developing data analysis methods to improve the characterization of the earth's physical processes using explainable data fusion techniques. There are three main research objectives of the PhD. First, understanding methods to select the most relevant datasets for any project for effective analysis by incorporating domain knowledge and state-of-the-art feature selection algorithms. Second, to investigate the data fusion techniques to combine these most relevant datasets, and third, to explore ways to quantify the degree of confidence of the fused product thereby dealing with uncertainty associated with remote sensing products. 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